A digitized photograph can be obtained by taking a photo with a digital camera or by digitizing a photograph taken with a film camera. Such a digitized photograph represents a two-dimensional array of brightness or pixel intensity values. These values may be measured by the camera in many cases. However, these pixel intensity values are rarely true measurements of relative radiance in the photographed scene. For example, if a first pixel has twice the measured pixel intensity of a second pixel, that does not usually mean that the first pixel observed twice the radiance of the second pixel. Instead, there is usually an unknown, nonlinear relationship called a radiometric camera response function that maps how radiance in the scene is related to pixel intensity values in the image.
The functional relationship between the camera's returned value and the actual amount of light entering the camera is unknown for any given camera. Radiometric calibration involves correlating the returned camera value to the amount of light entering the camera. This knowledge is important for applications including, for example, computer vision systems, image processing systems and theories and applications for noise reduction, object tracking, photometric stereo, shape from shading, high dynamic range imaging and estimation of reflectance and illumination from shape and brightness.
Many computer vision algorithms assume that image pixel intensities are linearly related to scene irradiance. For most cameras, however, this is not the case. Camera manufacturers often deliberately engineer nonlinear response functions into their cameras in order to match film characteristics or to account for nonlinear characteristics of computer displays and the human visual system. Even if the response is intended to be linear, the analog circuitry in the image sensors themselves introduce small nonlinearities. Thus, the camera's “radiometric response function,” which maps scene irradiance to measured pixel intensities, is generally a nonlinear function.
Calibration methods rely on some known relationship between irradiance at the camera image plane and measured pixel intensities. Prior approaches use a color checker chart with known reflectances, registered images with different exposure ratios, and the irradiance distribution along edges in images, for example.
Radiometric calibration methods generally require means to collect samples of the radiometric response function with some known relationship. For example, one approach is to use an image of a uniformly illuminated Macbeth color chart, which has color patches with known reflectances. A second approach calibrates using multiple registered images of a static scene with different known exposure times. The ratio of the irradiance at the same pixel in different images is equal to the ratio of the exposure times of the images. This relationship is used to solve for a parametric response function. A third method also uses multiple images with different exposures, but solves for a smooth non-parametric function. A fourth method requires only rough estimates of the exposure times and iteratively computes a polynomial inverse response function and more accurate estimates of the exposure times. A fifth method uses a statistical model of the charge-coupled device (CCD) image formation process to iteratively estimate non-parametric inverse response functions.
Several methods have been developed that use multiple exposures but do not require precise registration. A sixth method uses the relationship between the intensity histograms of two scenes imaged with different exposures because intensity histograms are relatively unaffected by small changes in the scene. A seventh method computes point correspondences between images. An eighth method estimates response functions from a rotating and zooming camera.
A ninth method computes the radiometric response function from red, green and blue (RGB) distributions along color edges in a single image. They use a prior model of response functions to compute the radiometric response function as the one which maps nonlinear measured RGB intensity distributions to linear ones. The researchers adapt this idea to grayscale images by measuring the nonuniformity of edge intensity histograms, using spatial mixtures. Recently, other researchers have proposed a tenth method to compute camera response functions from noise distributions. Analyzing noise distributions requires much input data, although a single image can be used under very carefully controlled conditions.
The discussion above is merely provided for general background information and is not intended for use as an aid in determining the scope of the claimed subject matter.